*Corresponding author e-mail: email@example.com A b s t r a c t. Several review articles have emphasized, that a comprehensive set of pedotransfer functions may be applied throughout a wide range of disciplines of Earth system scienc- es and are of great importance for land surface models. Most pedotransfer functions deducing soil hydraulic data from non- hydraulic soil data such as soil texture and bulk density, yield soil water retention predictions, but do not provide information concerning soil hydraulic conductivity. For this reason, a simple method was developed to estimate soil hydraulic conductivity using soil water retention information. Empirical equations are established to predict soil hydraulic conductivity from soil water retention information. These equations are relatively straight- forward and do not require the fitting of nonlinear functions. Predictions of soil hydraulic conductivity using 106 soil samples indicates the reliable performance of the new method. The predic- tion quality of the new method was estimated from the calibration data set, which produced equivalent results to the Zacharias and Wessolek pedotransfer function, which were even better than the predictions obtained from the original Mualem-van Genuchten model, the Soto fractal model, and the pedotransfer function reported by Weynants and Vereecken. The stochastic structure of the calibration data reflects the presence of important soil structur- al properties, which are not represented by the soil water retention characteristics.
A b s t r a c t. This paper presents the results of statistical- physical modelling (pedotransfer function) relating soil water content at defined values of soil water potential to selected physical and chemical parameters of organic soils. The two models were developed as the result of the modelling. The independent variables of equations of both models are: ash content, specific surface area, bulk density, pH in KCl and Fe content. The following ranges of determination coefficient values between the measured and predicted water content were estimated for the models: 0.67 < R 2 < 0.81 for the first and 0.68 < R 2 < 0.91 for second one.
The developed PTF for agriculture can predict the hydraulic conductivity with the use of a Digital Elevation Model (DEM). At this moment, DEMs with a resolution between 5m and 1000m are available. A 5m resolution DEM is not very common and can only be derived from LIDAR (light detection and ranging) (Agarwal P. K. et al., 2006). When using the PTF developed for this thesis, one should consider wisely which DEM resolution should be used as predictor. In section 3.1 it was already concluded that a 30m resolution DEM is very inaccurate for this research area, therefore a 5 or 10m DEM is recommended. DEM’s with higher resolution show less curves and become rapidly more linear when using higher resolution (Hancock, 2005). In coarser DEMs the steeper slopes are considered less steep and lower slopes are considered steeper, compared to higher resolution DEMs (Sorensen & Seibert, 2007).
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two statistical parameters, the mean square deviation  and the Nash Sutcliffe Efficiency parameter . A use of the Rosetta PTF is to assist researchers in studying the hydrological processes that could lead to better water management practices in the region of study. This is im- portant because in the Texas Southern High Plains the combination of common droughts and a declining water table from the Ogallala Aquifer are a challenge to man- age the irrigation of crops and establish crops under dry- land conditions [5,25].
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Firstly, the measured data points θ (h) for indi- vidual retention curves were parameterized, using the RETC computer program (van Genuchten et al. 1991) to obtain the fitted parameters θ s , α , and n. Secondly, the continuous pedotransfer functions of Wösten et al. (1998), as given in Table 3, were used to estimate SMRC parameters for each site. Thirdly, our own continuous pedotransfer func- tions were derived, using the data from all sites. The technique of derivation was similar to that used by Wösten et al. (1998). The input data for
Soil is a huge reservoir of water used by plants in periods without precipitation and signiﬁ cantly aﬀ ects the hydrological balance of any territory. To evaluate the hydrological balance of any piece of the land given, it is therefore necessary to carry out the analysis of the hydro-physical conditions. To speed up and simplify the determination of basic hydro-physical properties of soil man developed and began to use the so-called pedotransfer functions (PTF). Comparison of domestic and foreign works, however, faces problems such as the deﬁ nition of available water supplies in diﬀ erent countries. In the research project “Speciﬁ cation of the available supplies of nitrogen and water in the soil proﬁ le and determining the eﬀ ective depth of crops’ roots” we have dealt with the selection of suitable pedotransfer functions necessary for basic agricultural production with the requirement of minimal amount of input data. For our research we chose several PTFs developed and used in the Czech Republic for a long time, with a minimum of input data, and several new PTFs from foreign authors with greater correlation, but also a greater need of input data, and we compared each other. The best correlation between values and the pedotransfer function for the ﬁ eld water capacity and for the wilting point seems to be the PTF according to Tomasella and PTF according to Batjes. Pedotransfer function according to Váša, in terms of volume of input data, appears better.
Abstract. This paper presents a modeling study aiming at quantifying the possible impact of soil characteristics on the hydrological response of small ungauged catchments in a context of extreme events. The study focuses on the Septem- ber 2002 event in the Gard region (South-Eastern France), which led to catastrophic flash-floods. The proposed model- ing approach is able to take into account rainfall variability and soil profiles variability. Its spatial discretization is de- termined using Digital Elevation Model (DEM) and a soil map. The model computes infiltration, ponding and verti- cal soil water distribution, as well as river discharge. In or- der to be applicable to ungauged catchments, the model is set up without any calibration and the soil parameter spec- ification is based on an existing soil database. The model verification is based on a regional evaluation using 17 esti- mated discharges obtained from an extensive post-flood in- vestigation. Thus, this approach provides a spatial view of the hydrological response across a large range of scales. To perform the simulations, radar rainfall estimations are used at a 1 km 2 and 5 min resolution. To specify the soil hy- draulic properties, two types of pedotransfer function (PTF) are compared. It is shown that the PTF including information about soil structure reflects better the spatial variability that can be encountered in the field. The study is focused on four small ungauged catchments of less than 10 km 2 , which ex- perienced casualties. Simulated specific peak discharges are found to be in agreement with estimations from a post-event
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Mapping and quantifying SOC contents and distributions is crucial for modeling global carbon cycles (Wills, 2007). The role of organic matter, in soil fertility and crop productivity, is well documented by various research findings (Reddy et al., 2005; Meena et al., 2007). It affects the nutrients and moisture retention capacity in soils; this property enhances the plants’ nutrients uptake. Different laboratory methods can be used to determine the organic carbon; Walkley and Black wet oxidation are the common one; others are combustion and loss on ignition (Wills, 2007). However, these methods are expensive. Part of this work is suggesting the prediction of soil organic carbon using the pedotransfer function as an alternative to minimize analysis expenses in areas with enough baseline data for soil characterization.
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Abstract. Adequate water management is required to im- prove the efficiency and sustainability of agricultural sys- tems when water is scarce or over-abundant, especially in the case of land use changes. In order to quantify, to pre- dict and eventually to control water and solute transport into soil, soil hydraulic properties need to be determined pre- cisely. As their determination is often tedious, expensive and time-consuming, many alternative field and laboratory tech- niques are now available. The aim of this study was to de- termine unsaturated soil hydraulic properties under different land uses and to compare the results obtained with differ- ent measurement methods (Beerkan, disc infiltrometer, evap- oration, pedotransfer function). The study has been realized on a tropical sandy soil in a mini-watershed in northeastern Thailand. The experimental plots were positioned in a rub- ber tree plantation in different positions along a slope, in ruzi grass pasture and in an original forest site. Non-parametric statistics demonstrated that van Genuchten unsaturated soil parameters (K s , α and n) were significantly different ac-
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The results for the first model development indicated that bulk density, geometric mean particle-size diameter (d g ) and clay content were the main variables affecting water content retained at different matric potentials. However, sand content affects water contents near saturation (q 10 , q 33 , q 50 ), which is in agreement with previous results by Tomasella et al. (2003). Those authors found that coarse- textured fractions affect water content near saturation while fine fractions are related to lower water content. The developed pedotransfer function for estimating saturated water content presented a similar form as compared to the Vereecken et al. (1989) model with a small change in the coefficients. Within the context of the present study, saturated water content can be mainly affected by clay content and bulk density. However, some workers have assumed the saturated water content to be equal to soil total porosity multiplied by 0.93 (Williams et al., 1992) or 0.90 (Pachepsky et al., 1999) where total porosity is calculated from bulk density and particle density (r p =2.65).
In this study, multivariate linear regression and neural network model (feed-forward back- propagation network) were employed to develop a pedotransfer function for predicting soil cation exchange capacity by using available soil properties. This network was consisted of one hidden layer, a sigmoid activation function in hidden layer, and a linear activation function in output layer and Levenberg-Marquardt training algorithm used due to efficiency, simplicity and high speed. For predicting the soil property by means of PTFs, the input data were consisted of the percentages of clay and organic carbon for CEC. The performance of the multivariate linear regression and neural network model was evaluated using a test data set. Results showed that artificial neural network with seven neurons in hidden layer had better performance in predicting soil CEC than multivariate regression. The network model for this parameter was more suitable for capturing the non-linearity of the relationship between variables. ANN can model non-linear functions and have been shown to perform better than linear regression. With regarding to the evaluation criteria, the results of this study revealed that the artificial neural networks had
one of 11 possible USDA texture classes, then both class and continuous pedotransfer functions were developed. It is widely used (cited 435 times as of 26/10/15) and used in many comparative studies (like (Kværnø & Stolte, 2012; Liao et al., 2014)) in which it performs relatively well. A con of this PTF is that it has been developed for European soils, which can affect the performance on tropical soils. However, as the data set used to develop the PTFs is very large and its performance when used on different study areas has been good, the Wösten PTFs will be applied to the Keduang catchment. The PTF is a multivariate model with 44 parameters, which estimates the VG parameters using silt, clay, organic content, bulk density, and the Boolean variable ‘topsoil’ vs. ‘subsoil’. The PTF formulas developed by Wösten et al. are not as brief as the Van den Berg PTFs, and are not shown here. See Wösten et al. (1999).
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Abstract. Soil hydraulic properties are often derived indirectly, i.e. computed from easily available soil properties with pedotransfer functions (PTFs), when those are needed for catchment, regional or continental scale applications. When predicted soil hydraulic parameters are used for the modelling of the state and flux of water in soils, uncertainty of the computed values can provide more detailed information when drawing conclusions. The aim of this study was to update the previously published European PTFs (Tóth et al., 2015, euptf v1.4.0) by providing prediction uncertainty calculation built into the transfer functions. The new set of algorithms was derived for point predictions of soil water content at saturation (0 cm matric potential head), field capacity (both -100 and -330 cm matric potential head), wilting point (-15.000 cm matric potential head), plant available water, and saturated hydraulic conductivity, as well as the Mualem-van Genuchten model parameters of the moisture retention and hydraulic conductivity curve. The minimum set of input properties for the prediction is soil depth and sand, silt and clay content. The effect of including additional information like soil organic carbon content, bulk density, calcium carbonate content, pH and cation exchange capacity were extensively analysed. The PTFs were derived adopting the random forest method. The advantage of the new PTFs is that they i) provide information about prediction uncertainty, ii) are significantly more accurate than the euptfv1, iii) can be applied for more predictor variable combinations than the euptfv1, 32 instead of 5, and iv) are now also derived for the prediction of water content at -100 cm matric potential head and plant available water content.
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input variable for modelling the nonaqueous phase liquid trans- port and behaviour in the subsurface. In environmental and soil physical practice, it is mainly determined by scaling based on the water retention of soils or with charts of average empirical values of organic liquid retention or the fitting parameters of hydrau- lic functions. Predicting the fitting parameters of organic liquid retention curves with pedotransfer functions might be a promis- ing alternative method, but this topic has only been researched to a limited extent. Thus we investigated the applicability of dif- ferent hydraulic functions (3- and 4- parameter form of the van Genuchten equation and Brutsaert equation) for fitting organic liquid retention characteristics. Multivariate linear regression was used to build and develop pedotransfer functions, modelling relations between original and transformed values of basic soil properties and organic liquid retention. We attempted to gener- ate parametric pedotransfer functions. According to our results, the applicability of hydraulic functions for fitting nonaqueous phase liquid retention curves to the experimental data was proven. The investigations gave promising results for the possibility to estimate soil nonaqueous phase liquid retention with parametric pedotransfer functions.
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about soil properties (including for instance a parameterization of preferential flow and transport) and temporal information of meteorological variables (rainfall data with high temporal resolu- tion to capture rainfall intensities that trigger preferential flow) is to be preferred over a prediction with a much simpler model that considers only yearly rainfall amounts and uses informa- tion about soil texture and organic matter. The problem with the first approach is that an area-wide parameterization of a detailed model may not be possible due to a lack of data. For instance, detailed soil and weather data may not be available and the area-wide parameterization of preferential flow models still poses a problem, although recent advances have been made in the development of pedotransfer functions (see “Informing Soil Models Using Pedotransfer Functions” section) for these types of models (Moeys et al., 2012; Tiktak et al., 2012). The second problem is that computational resources may still be lim- iting to carry out simulations for millions of scenarios that are required to represent the distribution of soil, vegetation (crop), and weather conditions and to consider uncertainties or spatial variability of stochastic parameters that cannot be mapped. A workaround for this problem is to use meta-models that are calibrated on a limited number of simulation runs that are per- formed using more detailed models (Tiktak et al., 2006; Stenemo et al., 2007). Such meta-models are simple regression models that make a direct link between available input parameters and the model output of interest. The structure of the regression model can be based on analytical solutions of the process model that are obtained for certain boundary and initial conditions. Since they are much simpler, meta-models can easily be used to make predic- tions for a large number of scenarios and conditions. This allows evaluation of the effect of stochastic parameters
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zon designation (Ap, Ap1, Ap2, Apg, Ah, O, E, AB, Bw, Bg, Bs, Bt, Btg, BC, BCg, C/Ck/Cr and Cg) and statistics ap- plied in preparation for PTF application (Table 3).The min- imum number of replicates per horizon type was seven for the Bs horizon and the maximum number of replicates per horizon was 111 for Ap. Horizons Ap1 and Ap2 are gener- ally considered unique to Ap, this reflects the adoption of shallow till ploughing in some areas, however the bulk den- sities of both were similar, 1.044 and 1.072 g cm −3 , respec- tively. These designations were not unfounded as Ap hori- zons were generally lower (0.976 g cm −3 ) when compared to Ap1 and Ap2 horizons. The largest bulk density was in Cg horizons (1.566 g cm −3 ) and the lowest in the O horizons (0.329 g cm −3 ). The Bt horizons had the lowest standard de- viation and co-efficient of variation, 0.036 and 2.75 %, re- spectively. The O horizons had the largest co-efficient of vari- ation at 11.854 %.
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The database of soil hydrophysical properties in the Czech Republic called HYPRESCZ was created. It is based on the European database HYPRES, HYdraulic PRoperties of European Soils, and follows its structure with few modifications. It collects the available data from the Czech Republic from which pedotransfer functions (PTFs) for the estimation of soil hydrophysical properties from easily available soil properties can be derived and 2101 database entries were collected. The entries have different quality of data, out of the total number of entries 707 entries were applicable to PTFs derivation for the estimation of soil water retention curves (SWRCs). After elimination of replicates, finally 159 unique soil horizons (arable land only) were used for PTFs deriva- tion. The parametric continuous pedotransfer functions for estimation of SWRCs in the Czech Republic were derived within this study and are based on Wösten’s model. The retention curves were estimated using both these newly derived PTFs and Wösten’s original model, which was derived for European soils in general. The uncertainty of estimation was evaluated, employing the root mean squared error (RMSE) and the coefficient of determination (R 2 ) comparing the PTF-estimated and the directly fitted retention curves. The reliability of the
The database of soil hydrophysical properties in the Czech Republic (HYPRESCZ) which contains the data needed for derivation of pedotransfer functions for soil water retention curves was used for the estimation of field capacity and wilting point of agricultural land resource on a countrywide scale. The results were combined with the existing Soil Texture Map of the Czech Republic to create four new maps, namely the Map of Field Capacity and the Map of Wilting Point for the topsoil and subsoil separately. From the total number of 1048 relevant database entries, only about a half included reliable wilting point data. The k-Nearest computer code employing the k-Nearest neighbour technique was used for estimation of the missing wilting points, which made it possible to use all entries. The estimation uncertainty was assessed in terms of standard deviations and the root mean square error. Finally, two sets of class pedotransfer functions were derived and found sufficiently comparable: (i) the functions estimating the soil water retention curve in the whole range, derived solely from the database entries containing the measured wilting points, and (ii) the functions estimating the field capacity and wilting point only, derived from all database entries, including the k-Nearest neighbour estimated data. Keywords: field capacity; k-Nearest neighbour technique; pedotransfer functions; wilting point
Three types of pedotransfer functions are recognized . One category predicts soil moisture retention from basic soil properties. The other is based on point predic- tion of water retention characteristic as used by  to predict points along the moisture retention curve on Ira- nian soil. Another category involves prediction of pa- rameters in models describing the θ-h-k relationship. This latter approach used in the study involved pe- dotransfer functions that relate the simple and easy to measure soil properties to van Genutchen moisture reten- tion parameters, a notable gap in the science of develop- ment of pedotransfer functions. Most established pedo- transfer functions for predicting soil hydraulic character- istics from continuous soil properties are based on statis- tical regression  in which the response variable, y, predicted from a number of n predictor variables, x i
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Part-time faculty (PTF) have become increasingly important to the success community colleges. Gappa, Austin, and Trice (2007) estimate that 67 percent of faculty employed at two-year institutions are part-time. Hired not only for their practical, real-world experience, PTF allow institutions flexibility with respect to enrollment, courses offered, and budgeting—saving as much as 49 percent in instructional costs compared to hiring full-time faculty (Grubb, 1999). Given the fiscal constraints plaguing American higher education, coupled with an estimated 30-44 percent of full-time faculty at community colleges expected to retire within the next five to 10 years, it is likely that the hiring of PTF will escalate (Twombly, 2005). Although the increased used of PTF is warranted for these reasons, the literature suggests it comes at the expense of educational outcomes (Eagan & Jaeger, 2008; Jacoby, 2006; Jaeger & Eagan, 2008; Ronco & Cahill, 2006; Umbach, 2007). According to this research, while possessing the technical knowledge and skill needed for instruction at community colleges, PTF may employ less effective teaching methods (Bettinger & Long, 2010; Umbach, 2007), be unfamiliar with institutional policy and procedures (Jacobs, 1998), and lack in commitment (Jaeger & Eagan, 2008; Umbach, 2007).
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